






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
	raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
__UpperCAmelCase				: str						    =	logging.get_logger(__name__)

__UpperCAmelCase				: str						    =	"Hello world! cécé herlolip"





def        lowercase_			(     __snake_case  :				str						,						__snake_case  :				str						,						__snake_case  :				bool   )  ->			Tuple:





				'''simple docstring'''


				snake_case__   :List[str]       = FairseqRobertaModel.from_pretrained(a_   )
				roberta.eval()  # disable dropout
				snake_case__   :List[str]       = roberta.model.encoder.sentence_encoder
				snake_case__   :Tuple       = XLMRobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings						,						hidden_size=roberta.cfg.model.encoder_embed_dim						,						num_hidden_layers=roberta.cfg.model.encoder_layers						,						num_attention_heads=roberta.cfg.model.encoder_attention_heads						,						intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim						,						max_position_embeddings=5_14						,						type_vocab_size=1						,						layer_norm_eps=1e-5						,						)
				if classification_head:
								snake_case__   :Union[str, Any]       = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

				print("Our RoBERTa config:"						,						a_   )

				snake_case__   :List[str]       = XLMRobertaXLForSequenceClassification(a_   ) if classification_head else XLMRobertaXLForMaskedLM(a_   )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__   :List[str]       = roberta_sent_encoder.embed_tokens.weight
				snake_case__   :Tuple       = roberta_sent_encoder.embed_positions.weight
				snake_case__   :str       = torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.

				snake_case__   :Tuple       = roberta_sent_encoder.layer_norm.weight
				snake_case__   :int       = roberta_sent_encoder.layer_norm.bias

				for i in range(config.num_hidden_layers   ):
								# Encoder: start of layer
								snake_case__   :BertLayer       = model.roberta.encoder.layer[i]
								snake_case__   :TransformerSentenceEncoderLayer       = roberta_sent_encoder.layers[i]

								snake_case__   :RobertaAttention       = layer.attention
								snake_case__   :int       = roberta_layer.self_attn_layer_norm.weight
								snake_case__   :Optional[Any]       = roberta_layer.self_attn_layer_norm.bias

								# self attention
								snake_case__   :BertSelfAttention       = layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)   )
								)

								snake_case__   :Dict       = roberta_layer.self_attn.q_proj.weight
								snake_case__   :Dict       = roberta_layer.self_attn.q_proj.bias
								snake_case__   :Optional[Any]       = roberta_layer.self_attn.k_proj.weight
								snake_case__   :Tuple       = roberta_layer.self_attn.k_proj.bias
								snake_case__   :Union[str, Any]       = roberta_layer.self_attn.v_proj.weight
								snake_case__   :Dict       = roberta_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__   :BertSelfOutput       = layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								snake_case__   :Any       = roberta_layer.self_attn.out_proj.weight
								snake_case__   :List[Any]       = roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								snake_case__   :Any       = roberta_layer.final_layer_norm.weight
								snake_case__   :Any       = roberta_layer.final_layer_norm.bias

								# intermediate
								snake_case__   :BertIntermediate       = layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__   :Dict       = roberta_layer.fca.weight
								snake_case__   :Union[str, Any]       = roberta_layer.fca.bias

								# output
								snake_case__   :BertOutput       = layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__   :Optional[Any]       = roberta_layer.fca.weight
								snake_case__   :Tuple       = roberta_layer.fca.bias
								# end of layer

				if classification_head:
								snake_case__   :List[str]       = roberta.model.classification_heads["mnli"].dense.weight
								snake_case__   :List[str]       = roberta.model.classification_heads["mnli"].dense.bias
								snake_case__   :Optional[Any]       = roberta.model.classification_heads["mnli"].out_proj.weight
								snake_case__   :Union[str, Any]       = roberta.model.classification_heads["mnli"].out_proj.bias
				else:
								# LM Head
								snake_case__   :Dict       = roberta.model.encoder.lm_head.dense.weight
								snake_case__   :List[str]       = roberta.model.encoder.lm_head.dense.bias
								snake_case__   :Dict       = roberta.model.encoder.lm_head.layer_norm.weight
								snake_case__   :Dict       = roberta.model.encoder.lm_head.layer_norm.bias
								snake_case__   :int       = roberta.model.encoder.lm_head.weight
								snake_case__   :Dict       = roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__   :torch.Tensor       = roberta.encode(a_   ).unsqueeze(0   )  # batch of size 1

				snake_case__   :Union[str, Any]       = model(a_   )[0]
				if classification_head:
								snake_case__   :int       = roberta.model.classification_heads["mnli"](roberta.extract_features(a_   )   )
				else:
								snake_case__   :Optional[Any]       = roberta.model(a_   )[0]
				print(our_output.shape						,						their_output.shape   )
				snake_case__   :Optional[int]       = torch.max(torch.abs(our_output - their_output   )   ).item()
				print(F'max_absolute_diff = {max_absolute_diff}'   )  # ~ 1e-7
				snake_case__   :Union[str, Any]       = torch.allclose(a_						,						a_						,						atol=1e-3   )
				print("Do both models output the same tensors?"						,						"🔥" if success else "💩"   )
				if not success:
								raise Exception("Something went wRoNg"   )

				pathlib.Path(a_   ).mkdir(parents=a_						,						exist_ok=a_   )
				print(F'Saving model to {pytorch_dump_folder_path}'   )
				model.save_pretrained(a_   )


if __name__ == "__main__":
	__UpperCAmelCase				: Union[str, Any]						    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	parser.add_argument(
	    "--classification_head", action="store_true", help="Whether to convert a final classification head."
	)
	__UpperCAmelCase				: int						    =	parser.parse_args()
	convert_xlm_roberta_xl_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)






'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



